There’s an odd milestone for any new company. It’s when you’ve worked with enough customers and prospects to understand their misunderstandings about what you do. Only then do you begin to see your own product through customer eyes—to see, finally, the assumptions you might make about why or how a product works, if you weren’t living and breathing that product every day.

Here, in no particular order, are the five misconceptions we’ve learned to listen for.

“I bet this requires everyone in engineering to change the way they work.”

I said “in no particular order”, but this is probably our top misconception: that for Pinpoint to work its magic, people and teams have to input something, or remember to do something, or generally work in ways unnatural to the ways they’re currently working.


It’s our job to synthesize the raw activity data from the systems you already use—invisibly and seamlessly. The only changes to the way you work are the ones you and your teams decide, based on the performance views we show.

“You probably assume we’re using the same delivery methodology everywhere.”

We don’t. That’s rare, frankly. Most of our customers are some mix of “Agile” (which itself has many flavors), kanban, and/or no set methodology at all.

We’re entirely process-agnostic. We’d be idiots if we weren’t. Assuming or attempting to force-feed some methodology onto other people’s engineering teams is a recipe for oblivion.

We look at software engineering as a pipeline: ideas enter, and what comes out is quality, working software. We’re concerned with showing you how well your engineering pipeline works—regardless of the methodologies it might contain. In the process of this, you’re likely to discover which type of method(s) work best. But any changes are up to you.

“Our system use is too messy and inconsistent to give usable data.”

For us, there’s no such thing as unusable data, and no need for you to undertake some kind of data cleansing before using Pinpoint. Put another way: there’s no need to clean up before housekeeping arrives.

We understand the messy reality of most engineering departments. Actually, that’s part of why we exist. The signals we derive don’t depend on any kind of organizational consistency in working methods. When we activate the first performance views for your teams—we call it the “Lights On” moment—you’ll see automatically and instantly all your system usage patterns, including ones you may want to change or correct.

“Is this going to take six months and two dedicated admins to set up?”

If it does, you can shoot us first. Generally, activation requires less than an hour. It’s the job of our Agent to harness all the raw metadata we need; our machine intelligence does the rest.

“This sounds like Big Brother coming to engineering.”

No. This is effective coaching coming to engineering. Our signals don’t exist to punish anyone; they exist to help people, teams, and leaders find new performance gears. This is why we like the athletic comparison so much: advanced analytics like Sabermetrics are used to find hidden performers, and to quantify what really helps performance.

Without hard data, too many decisions in engineering—including performance reviews—are made on gut feel. Pinpoint cuts through the guesswork (and office politics) to show how work is done in an organization, who’s contributing and how, and what actions will unlock higher performance for everyone.

Get the data science behind high-performance teams.